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Automation of Smart Homes with Multiple Rule Sources

Kaufman Eran, Yigal Hoffner

TL;DR

The paper addresses the challenge of automating smart homes when multiple stakeholders can contribute rules. It introduces a policy-driven Rule Administrator, a domain-specific rule language, and a learning component that derives rules from resident behavior using Markovian networks, enabling recommendations and automatic updates. The architecture combines a Data Flow Channel and a Control Flow Channel within a three-layer, feedback-driven framework, enabling high-level rules to be translated into device actions while decoupling from specific hardware. Security and privacy are addressed via ACL-based access control, authentication, and authorization, with a priority-based mechanism to resolve rule conflicts among diverse rule owners. A pilot implementation demonstrates the feasibility of the approach with a distributed system that integrates cloud and local components and real IoT devices, highlighting portability and policy-driven governance as practical gains for multi-stakeholder smart homes.

Abstract

Using rules for home automation presents several challenges, especially when considering multiple stakeholders in addition to residents, such as homeowners, local authorities, energy suppliers, and system providers, who will wish to contribute rules to safeguard their interests. Managing rules from various sources requires a structured procedure, a relevant policy, and a designated authority to ensure authorized and correct contributions and address potential conflicts. In addition, the smart home rule language needs to express conditions and decisions at a high level of abstraction without specifying implementation details such as interfaces, access protocols, and room layout. Decoupling high-level decisions from these details supports the transferability and adaptability of rules to similar homes. This separation also has important implications for structuring the smart home system and the security architecture. Our proposed approach and system implementation introduce a rule management process, a rule administrator, and a domain-specific rule language to address these challenges. In addition, the system provides a learning process that observes residents, detects behavior patterns, and derives rules which are then presented as recommendations to the system.

Automation of Smart Homes with Multiple Rule Sources

TL;DR

The paper addresses the challenge of automating smart homes when multiple stakeholders can contribute rules. It introduces a policy-driven Rule Administrator, a domain-specific rule language, and a learning component that derives rules from resident behavior using Markovian networks, enabling recommendations and automatic updates. The architecture combines a Data Flow Channel and a Control Flow Channel within a three-layer, feedback-driven framework, enabling high-level rules to be translated into device actions while decoupling from specific hardware. Security and privacy are addressed via ACL-based access control, authentication, and authorization, with a priority-based mechanism to resolve rule conflicts among diverse rule owners. A pilot implementation demonstrates the feasibility of the approach with a distributed system that integrates cloud and local components and real IoT devices, highlighting portability and policy-driven governance as practical gains for multi-stakeholder smart homes.

Abstract

Using rules for home automation presents several challenges, especially when considering multiple stakeholders in addition to residents, such as homeowners, local authorities, energy suppliers, and system providers, who will wish to contribute rules to safeguard their interests. Managing rules from various sources requires a structured procedure, a relevant policy, and a designated authority to ensure authorized and correct contributions and address potential conflicts. In addition, the smart home rule language needs to express conditions and decisions at a high level of abstraction without specifying implementation details such as interfaces, access protocols, and room layout. Decoupling high-level decisions from these details supports the transferability and adaptability of rules to similar homes. This separation also has important implications for structuring the smart home system and the security architecture. Our proposed approach and system implementation introduce a rule management process, a rule administrator, and a domain-specific rule language to address these challenges. In addition, the system provides a learning process that observes residents, detects behavior patterns, and derives rules which are then presented as recommendations to the system.
Paper Structure (25 sections, 2 equations, 9 figures)

This paper contains 25 sections, 2 equations, 9 figures.

Table of Contents

  1. INTRODUCTION
  2. Requirements of a Multi-Rules-Source Smart Home
  3. OVERVIEW OF RELATED SYSTEMS
  4. THE SMART HOME ARCHITECTURE
  5. The Data Flow Channel
  6. The Control Flow Channel
  7. Combining the Data Flow and Control Flow channels
  8. Management of End-Point Devices
  9. Manual Overriding of Home State.
  10. THE RULE LANGUAGE
  11. THE RULE MANAGEMENT
  12. THE LEARNING PROCESS
  13. Related Work
  14. Our Learning Process Implementation
  15. Reasoning Premise
  16. ...and 10 more sections

Figures (9)

  • Figure 1: The layered architecture of the smart-home system with three levels of feedback loops.
  • Figure 2: The Data Flow channel, showing the stages that transform the data from the sensors into more comprehensive views of the system.
  • Figure 3: The Control Flow channel and the translation between the layers going from the rules, through the decisions, to the activation of devices.
  • Figure 4: The overall architecture of the smart home system shows both control flow and data flow channels.
  • Figure 5: Different management configurations of the actuator devices using the SET and KEEP commands.
  • ...and 4 more figures